scholarly journals DiseaseLinc: Disease Enrichment Analysis of Sets of Differentially Expressed LincRNAs

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 751
Author(s):  
Piyush More ◽  
Sweta Talyan ◽  
Jean-Fred Fontaine ◽  
Enrique M. Muro ◽  
Miguel A. Andrade-Navarro
Keyword(s):  
Enrichment Analysis ◽  
Analysis Tool ◽  
Original Function ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Copies ◽  
Parental Gene ◽  
Non Coding Rnas ◽  
Analysis Of Function ◽  
Genomic Duplication

Long intergenic non-coding RNAs (LincRNAs) are long RNAs that do not encode proteins. Functional evidence is lacking for most of them. Their biogenesis is not well-known, but it is thought that many lincRNAs originate from genomic duplication of coding material, resulting in pseudogenes, gene copies that lose their original function and can accumulate mutations. While most pseudogenes eventually stop producing a transcript and become erased by mutations, many of these pseudogene-based lincRNAs keep similarity to the parental gene from which they originated, possibly for functional reasons. For example, they can act as decoys for miRNAs targeting the parental gene. Enrichment analysis of function is a powerful tool to discover the functional effects of a treatment producing differential expression of transcripts. However, in the case of lincRNAs, since their function is not easy to define experimentally, such a tool is lacking. To address this problem, we have developed an enrichment analysis tool that focuses on lincRNAs exploiting their functional association, using as a proxy function that of the parental genes and has a focus on human diseases. The tool is available at: http://cbdm-01.zdv.uni-mainz.de:3838/piyusmor/DiseaseLinc/.

scholarly journals Effect of flapless osteoperforation-assisted tooth movement on atrophic alveolar ridge: Histomorphometric and gene-enrichment analysis

2017 ◽  
Vol 88 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Ji-Won Lee ◽  
Jung-Yul Cha ◽  
Ki-Ho Park ◽  
Yoon-Goo Kang ◽  
Su-Jung Kim
Keyword(s):  
Signaling Pathway ◽  
Tooth Movement ◽  
Osteoclast Differentiation ◽  
Wnt Signaling Pathway ◽  
Enrichment Analysis ◽  
Tissue Response ◽  
Alveolar Ridge ◽  
Mineral Density ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

ABSTRACT Objective: To investigate the effect of flapless osteoperforation on the tissue response of the atrophic alveolar ridge affected by orthodontic tooth movement (OTM). Materials and Methods: An atrophic alveolar ridge model was established in the mandibular quadrants of eight beagle dogs. As a split-mouth design, the quadrants were randomly divided into group C (OTM only) and group OP (OTM with flapless osteoperforation). The rate of OTM for 10 weeks was compared between groups, and micro-CT-based histomorphometric analysis and RNA-sequencing-based gene-enrichment analysis were performed targeting the atrophic ridge. Results: Group OP displayed more rapid tooth movement with lower bone mineral density and higher trabecular fraction in the atrophic ridge than did group C, showing no intergroup difference of total ridge volume. As contributing biological functional pathways in group OP, the genes related to osteoclast differentiation and TNF signaling pathway were up-regulated and those associated with Wnt signaling pathway and AMPK signaling pathway were down-regulated. Conclusions: Flapless osteoperforation facilitated the rate of OTM toward the atrophic ridge, maintaining low bone density, whereas it did not increase the volume of the atrophic ridge.

scholarly journals Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions

2008 ◽  
Vol 36 (7) ◽  
pp. e43-e43 ◽  
Author(s):  
K. De Preter ◽  
R. Barriot ◽  
F. Speleman ◽  
J. Vandesompele ◽  
Y. Moreau
Keyword(s):  
High Resolution ◽  
Enrichment Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Sets

scholarly journals A Network Pharmacology Approach to Uncover the Multiple Mechanisms of Hedyotis diffusa Willd. on Colorectal Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinkui Liu ◽  
Jiarui Wu ◽  
Dan Zhang ◽  
Kaihuan Wang ◽  
Xiaojiao Duan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Chinese Herb ◽  
Endocrine System ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Hedyotis Diffusa

Background. As one of the most frequently diagnosed cancer diseases globally, colorectal cancer (CRC) remains an important cause of cancer-related death. Although the traditional Chinese herb Hedyotis diffusa Willd. (HDW) has been proven to be effective for treating CRC in clinical practice, its definite mechanisms have not been completely deciphered. Objective. The aim of our research is to systematically explore the multiple mechanisms of HDW on CRC. Methods. This study adopted the network pharmacology approach, which was mainly composed of active component gathering, target prediction, CRC gene collection, network analysis, and gene enrichment analysis. Results. The network analysis showed that 10 targets might be the therapeutic targets of HDW on CRC, namely, HRAS, PIK3CA, KRAS, TP53, APC, BRAF, GSK3B, CDK2, AKT1, and RAF1. The gene enrichment analysis implied that HDW probably benefits patients with CRC by modulating pathways related to cancers, infectious diseases, endocrine system, immune system, nervous system, signal transduction, cellular community, and cell motility. Conclusions. This study partially verified and predicted the pharmacological and molecular mechanism of HDW against CRC from a holistic perspective, which will also lay a foundation for the further experimental research and clinical rational application of HDW.

scholarly journals Network pharmacology-based elucidation of the molecular mechanism underlying the anti-migraine effect of Asari Radix et Rhizoma

2021 ◽  
Vol 18 (10) ◽  
pp. 2067-2074
Author(s):  
Yun-Bin Jiang ◽  
Mei Zhong ◽  
Ting Huang ◽  
Zhong-Hua Dai ◽  
Xing-Bao Tao ◽  
...  
Keyword(s):  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Tnf Gene

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine.

scholarly journals From Protein Variations to Biological Processes and Pathways with NET-GE

2017 ◽  
Vol 3 (3) ◽  
pp. 45 ◽  
Author(s):  
Samuele Bovo ◽  
Pietro Di Lena ◽  
Pier Luigi Martelli ◽  
Piero Fariselli ◽  
Rita Casadio
Keyword(s):  
Biological Networks ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Biological Processes ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Hyperactivity Disorder ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Common Technique

Gene enrichment analysis is a common technique for highlighting molecular pathways and biological processes of a phenotype. Such technique has recently evolved exploiting the information contained in biological networks. We developed NET-GE, a web server for network-based gene enrichment analyses. NET-GE defines functional associations between a list of genes/proteins and biological processes or pathways by identifying function-specific modules in a molecular interaction network. The peculiarity of NET-GE is the possibility to enrich terms not detectable by standard enrichment procedure. Here, we highlight with two specific applications the performances of NET-GE by computing which functional phenotypes can be associated with two different sets of genes related to Attention Deficit Hyperactivity Disorder and to an Obsessive-compulsive disorder, respectively.

scholarly journals NETGE-PLUS: standard and network-based gene enrichment analysis in human and model organisms

2019 ◽  
Author(s):  
Samuele Bovo ◽  
Pier Luigi Martelli ◽  
Pietro Di Lena ◽  
Rita Casadio
Keyword(s):  
Complex Traits ◽  
Enrichment Analysis ◽  
Data Retrieval ◽  
Functional Enrichment ◽  
Model Organisms ◽  
Functional Interpretation ◽  
Kegg Pathways ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Gene Sets

ABSTRACTOmics techniques provide a spectrum of information that needs to be disentangled to characterize complex traits at the molecular level. The gap between genotype and phenotype must be closed by reconciling the genome information with the set of molecular pathways and biological processes describing the phenotype. In dealing with this problem, gene enrichment analysis has become the most widely adopted strategy. Here, we present NETGE-PLUS, a web-server for standard and network-based functional interpretation of gene sets of human and of model organisms, including S. scrofa, S. cerevisiae, E. coli and A. thaliana. NETGE-PLUS enables the functional enrichment of both simple and ranked lists of genes, also introducing the possibility of exploring relationships among KEGG pathways. A web interface makes data retrieval complete and user-friendly. NETGE-PLUS is publicly available at http://net-ge2.biocomp.unibo.it

scholarly journals Cluster analysis of transcriptomic datasets to identify endotypes of Idiopathic Pulmonary Fibrosis

2021 ◽  
Author(s):  
Luke Michael Kraven ◽  
Adam R Taylor ◽  
Phil Michael Molyneaux ◽  
Toby M Maher ◽  
John E McDonough ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Personalised Medicine ◽  
Enrichment Analysis ◽  
Innate And Adaptive Immunity ◽  
Blood Gene Expression ◽  
Mitochondrial Homeostasis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Distinct Disease

Rationale: Considerable clinical heterogeneity in Idiopathic Pulmonary Fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes could allow for a biomarker-driven personalised medicine approach in IPF. Objectives: To improve our understanding of the pathogenesis of IPF by identifying clinically distinct groups of patients with IPF that could represent distinct disease endotypes. Methods: We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets (total 220 IPF cases). We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets (total 194 IPF cases). Measurements and main results: We identified three clusters of IPF patients with statistically significant differences in lung function (P=0.009) and mortality (P=0.009) between groups. Gene enrichment analysis implicated dysregulation of mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: hazard ratio= 4.25, 95% confidence interval= [2.14, 8.46], P=3.7×10-5). Conclusions: We have identified blood gene expression signatures capable of discerning groups of IPF patients with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF.

scholarly journals Derivation and Comprehensive Analysis of Aging Patterns in Patients with Bladder Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Bin Wang ◽  
Fachun Tong ◽  
Chengxi Zhai ◽  
Long Wang ◽  
Yunzan Liu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Survival Prediction ◽  
Hub Gene ◽  
Cox Regression Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Differential Genes

Background. Aging is an essential risk factor for cancer. However, aging-related genes (ARGs) have not been comprehensively analyzed in bladder cancer (BC). Therefore, the study is aimed at derivating a risk stratification system for BC patients based on ARGs. Methods. Public databases were used to acquire ARGs sets, transcriptome files, and clinical data. The “limma” package was then used to screen for differential ARGs while also using univariate Cox regression analysis to explore for prognostic ARGs. The “ConsensusClusterPlus” package was used to perform aging patterns in BC patients based on the above prognostic ARGs. Subsequently, aging patterns were investigated in survival prediction, mutation landscape, immunotherapy, immunological checkpoints, and immune microenvironment. We likewise utilized gene enrichment analysis to explore the biological functions that were behind the findings. To construct a risk signature and nonogram for prognostic prediction, we used LASSO and Cox regression analysis based on differential genes in aging patterns. In addition, we plotted a nomogram and validate the accuracy of the risk signature in GEO and TCGA cohorts. We explored the possible biological mechanism using GSEA analysis and preliminarily identified a hub gene using PPI network. Finally, we validated the expression of hub gene in BC cell lines. Results. We screened 84 downregulated ARGs, 74 upregulated ARGs, and 32 prognostic ARGs in the human aging genome resource. The aging patterns based on prognostic genes had excellent survival prediction ( p < 0.001 ) and discriminatory ability in 405 BC patients. In addition, we found no significant differences in aging patterns in mutation analysis, which were all characterized by TP53, TTN, and KMT2D mutations. It is worth noting that cluster B in the aging patterns has a better response to immunotherapy and a more active immune microenvironment ( p < 0.05 ). In addition, gene enrichment analysis showed that aging patterns may be related to biological processes such as Staphylococcus aureus infection, phagosome, and cytokine-cytokine receptor interaction. Subsequently, we constructed a risk signature based on 16 differential genes from different aging patterns and had good survival prediction ability in both GEO and TCGA cohort. Specifically, survival analysis revealed a significantly shorter survival time in the high-risk group than in the low-risk group (TCGA and GEO, p < 0.001 ). In addition, AUC values in the ROC analysis predicted 1, 3, and 5 years in TCGA cohort that are 0.713, 0.714, and 0.738, respectively. AUC values predicted 1, 3, and 5 years in GEO cohort that are 0.606, 0.663, and 0.718, respectively. There is no doubt that risk score was an independent prognostic factor from results of multivariate Cox regression analysis in BC patients ( p < 0.001 ). There were also significant differences in immune cell infiltration, immune checkpoint, and immune score between the two groups ( p < 0.05 ), but it should not be ignored that the correlation with the HLA expression was weak. Finally, we identified and validated CLIC3 as a hub gene that may be involved in the Wnt signaling pathway, etc. Conclusion. We provided robust evidences that aging patterns based on ARGs can guide targeted therapy and survival prediction in BC patients.

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